Master data management continues to be plagued by poor data quality, costing many organizations time, money, lost customers, lost reputation and inefficiencies. Introduction In several surveys, taken by various research organizations about master data management and data quality, on average, 83% of respondents reported that their organizations have suffered problems due to poor master data. Business trends such as the ever-increasing
Metrics and the measurements they create are essential to the success of every data governance program and every data stewardship effort. Introduction Organizations that implement any new initiative must be able to measure its success so the program’s leadership can deliver progress reports to stakeholders and sponsors. Communicating success based on measured facts enables the program to demonstrate its effectiveness,
Abstraction is a powerful design tactic for creating flexible, robust and scalable data warehouse data models Introduction I remember meandering through the large galleries of a modern art museum, and stopping in my tracks in front of a very large canvas painted completely red with a single white dot in the center. The title was “City Skyline”, and as hard
Standards are different from guidelines. Standards are firm and must be followed. Successful data warehouses use standards Introduction Many dog owners give their dogs what they consider to be commands. They are really more like guidelines. (“Boscoe come!… pause, pause, pause… Well I guess Boscoe is busy with his chewy toy and doesn’t want to come just now.”) A number
Metadata management is one of the foundational components of an enterprise data management initiative. Many organizations struggle with its incorporation into their data management processes. Introduction Metadata is “data about data.” It is the context for the raw data content. As stated in the DAMA-DMBOK©, metadata can be thought of with the metaphor of a library’s card catalog. The card
Enterprise Data Management is the global function that facilitates the management of data as a valuable asset of an enterprise. Its components align to provide the capabilities to manage data as an organization resource. Introduction Enterprise Data Management (EDM) is the global function that facilitates the management of data as a valuable asset of an enterprise. As defined in the
Logical and physical data modeling are essential components of every organization’s enterprise data architecture, and should form the foundation of every database design. Standard techniques for logical and physical data modeling enable consistent development and usability. Even though the concept of data modeling has been around for a long time, in many organizations it is interpreted differently. According to the
Organizations can find increased value in business intelligence and analytics efforts through the use of various data delivery strategies. Introduction Like many organizations, your company probably has invested significant time, effort, and money into establishing your data warehousing and business intelligence environment. You’ve built the databases and the ETL programs to move, clean and integrate data from multiple sources into
Many project management challenges are centered on the people involved in a project; part of the “people, process, technology” triangle as the basis of all organizations Introduction There are still some common project management challenges, despite the huge efforts from the project management consortiums, project management Subject Matter Experts (SMEs), and many companies that are embracing project management as a
Challenges to successful data governance deployment are numerous, but can be avoided or overcome through attention to best practices and industry standards Introduction Corporate interest in data governance often starts with recognition within data integration projects that there is a high variability in success and that this can be attributed, partially, to a lack of standardized approaches to data management. Historically
